CellChat is an open-source R package that infers and analyzes intercellular communication from single-cell RNA-sequencing (scRNA-seq) data. It uses a comprehensive database of ligand-receptor interactions, including heteromeric complexes and cofactors, to predict signaling pathways and their roles in cellular functions. CellChat can classify signaling pathways, identify conserved and context-specific pathways, and visualize autocrine and paracrine signaling interactions. It also provides quantitative analysis of intercellular communication networks using network analysis, pattern recognition, and manifold learning. CellChat has been applied to mouse and human skin datasets to extract complex signaling patterns, revealing the role of TGFβ and ncWNT pathways in skin wound healing and morphogenesis. It has also identified key signaling events in embryonic skin development, including the role of FGF, WNT, and EDN pathways in melanocyte migration and hair follicle formation. CellChat can analyze signaling changes across different contexts, such as embryonic development and wound healing, and has been used to compare signaling patterns between embryonic and adult skin. It has also been applied to human skin datasets to identify signaling changes in atopic dermatitis, revealing enriched chemokine signals in lesional skin. Overall, CellChat provides a versatile toolkit for discovering novel intercellular communications and building cell-cell communication atlases in diverse tissues.CellChat is an open-source R package that infers and analyzes intercellular communication from single-cell RNA-sequencing (scRNA-seq) data. It uses a comprehensive database of ligand-receptor interactions, including heteromeric complexes and cofactors, to predict signaling pathways and their roles in cellular functions. CellChat can classify signaling pathways, identify conserved and context-specific pathways, and visualize autocrine and paracrine signaling interactions. It also provides quantitative analysis of intercellular communication networks using network analysis, pattern recognition, and manifold learning. CellChat has been applied to mouse and human skin datasets to extract complex signaling patterns, revealing the role of TGFβ and ncWNT pathways in skin wound healing and morphogenesis. It has also identified key signaling events in embryonic skin development, including the role of FGF, WNT, and EDN pathways in melanocyte migration and hair follicle formation. CellChat can analyze signaling changes across different contexts, such as embryonic development and wound healing, and has been used to compare signaling patterns between embryonic and adult skin. It has also been applied to human skin datasets to identify signaling changes in atopic dermatitis, revealing enriched chemokine signals in lesional skin. Overall, CellChat provides a versatile toolkit for discovering novel intercellular communications and building cell-cell communication atlases in diverse tissues.